We could include this preference into the model by defining the following loss function, This function penalizes the wrong prediction decreasing recall and that decreasing precision with  two weights c r and c p respectively.

we denote the loss function with c p /c r = 2 and that with c r /c p = 2 by p p and r p , respectively.


We made use of the same data set as introduced in Ding et al., 2008).

the data set includes about 591 threads from the forum TripAdvisor 2 .


By inspecting the clickthrough logs, we observed that unseen query-url pairs are often composed of informative previously observed subsequences.

we saw that query n-grams can be correlated with sequences of URL n-grams.


to what extent the similarity between two documents according to a feature implies that both mention the same person.


First, we use each feature combinations as the input for a Machine Learning algorithm.

we use a Decision Tree algorithm and WePS-1 data for training and WePS-2 data for testing.


x n }, a perfect Machine Learning algorithm would learn to always "listen" to the features giving correct information and ignore the features giving erroneous information.

if at least one feature gives correct information, then the perfect algorithm would produce a correct output.


As with the previous work of ( Li and Roth, 2002;Li and Roth, 2006;Krishnan et al., 2005;Moschitti et al., 2007), we propose a feature driven statistical question classifier (Huang et al., 2008).

we propose head word feature and augment semantic features of such head words using Word-Net.


We use Stanford named entity recognizer (NER) (Finkel et al., 2005) to identify CoNLL style NEs 7 as possible answer strings in a candidate sentence for a given type of question.

if the question is ABBR type, we tag CoNLL LOC, ORG and MISC entities as candidate answers; If the question is HUMAN type, we tag CoNLL PER and ORG entities; And if the question is LOC type, we tag CoNLL LOC and MISC entities.


For all paired paths between a question and a candidate sentence, if at least one pair of path in which all pairs of grammatical relations have been seen in the training, then the DEP feature is set to be true, false otherwise.

the true validity feature indicates that at least one pair of path between the question and candidate sentence is possible to be a true pair (ie, the candidate noun phrase in the sentence path is the true answer).


In this paper we extend the definition of g(x, y) to include features that are induced from unlabeled data.


In our experiments we use the 1-best MIRA algorithm    parameter-estimation method for the second-order parsing model.

we perform the following optimizations on each update t = 1, ..., T for re-estimating w and v: where L(y i , y) represents the loss between correct output of i'th sample y i and y.


Note that there are some similarities between our two-stage semi-supervised learning approach and the semi-supervised learning method introduced by (Blitzer et al., 2006), which is an extension of the method described by (Ando and Zhang, 2005).

both methods use a two-stage approach; They first train generative models or auxiliary problems from unlabeled data, and then, they incorporate these trained models into a supervised learning algorithm as real valued features.


To provide richer information, we consider more words besides word pairs.

we use subtrees containing two or three words extracted from dependency trees in the auto-parsed data.


We also demonstrate that our approach and other improvement techniques Nivre and Mc-Donald, 2008) are complementary and that we can achieve very high accuracies when we combine our method with other improvement techniques.

we achieve the best accuracy for the Chinese data.


Then we retrieve these subtrees in L st to get their set IDs (if a subtree is not included in L st , its set ID is ZERO.

we have four sets: HF, MF, LF, and ZERO.).


Even if the reviews contain mostly subjective material, the most prominent dimension may still not be sentiment, due to the fact that many reviews are sentimentally ambiguous.

a reviewer may have negative opinions on the actors but at the same time talk enthusiastically about how much she enjoyed the plot.


In comparison to the aforementioned feedback mechanisms, ours is arguably much simpler: we only require that the user select a dimension by examining a small number of features for each dimension, as opposed to having the user generate the feature space in an interactive manner or identify clusters that need to be merged or split.

identifying clusters for merging or splitting in Balcan and Blum's algorithm may not be as easy as it appears: for each MERGE or SPLIT decision the user makes, she has to sample a large number of documents from the cluster(s), read through the documents, and base her decision on the extent to which the documents are (dis)similar to each other.


Recall that a maximum margin classifier (e.g., a support vector machine) separates data points from two classes while maximizing the margin of separation.

a maximum margin hyperplane is defined by w • x − b = 0, where x is a feature vector representing an arbitrary data point, and w (a weight vector) and b (a scalar) are parameters that are learned by solving the following constrained optimization problem: where c i ∈ {+1, −1} is the class of the i-th training point x i , ξ i is the degree of misclassification of x i , and C is a regularization parameter that balances training error and model complexity.


Note that a feature with a large positive weight is strongly indicative of the positive class, whereas a feature with a large negative weight is strongly indicative of the negative class.

the most informative features are those with large absolute weight values.


Recall that the method we proposed represents each dimension with a small number of features and asks a user to select the desired dimension by inspecting the corresponding feature lists.

each feature list serves as a "summary" of its corresponding dimension, and inspecting the features induced for each dimension can give us insights into the different dimensions of a dataset.


Inspecting the induced features, we can determine that it has a sentiment dimension (e 2 ), as well as a humor vs. thriller dimension (e 4 ).

if we cluster along e 2 , we get a sentiment-based clustering; and if we cluster along e 4 , we obtain a genre-based (humor vs. thriller) clustering.


In this paper, we propose a novel method based on integer linear programming that can adapt an existing lexicon into a new one to reflect the characteristics of the data more directly.

our method collectively considers the relations among words and opinion expressions to derive the most likely polarity of each lexical item (positive, neutral, negative, or negator) for the given domain.


Polarity lexicons have been a valuable resource for sentiment analysis and opinion mining.

they have been an essential ingredient for finegrained sentiment analysis (e.g., Kim and Hovy (2004), Kennedy and Inkpen (2005), ).


In this paper, we propose a novel method based on integer linear programming to adapt an existing polarity lexicon into a new one to reflect the characteristics of the data more directly.

our method considers the relations among words and opinion expressions collectively to derive the most likely polarity of each word for the given domain.


The first are word-to-word relations within each expression.

if we are not sure about the polarity of a certain word, we can still make a guess based on the polarities of other words within the same expression and knowledge of the polarity of the expression.


Because β l is associated with a negative weight in the objective function, the ILP solver will then assign '0' to β l to maximize the objective function.

we encourage the ILP solver to modify the original lexicon for the given expression e l .


Next we encode a constraint that is the opposite of the previous one.

if the polarity classification algorithm f (e l , L) makes a correct prediction on e l using the original lexicon L, then we encourage ILP to keep the original word-level polarities for words in e l .


In the experiment section, we seek for answers for the following questions: Q1 What is the effect of a polarity lexicon on the expression-level polarity classification task?

is it useful when using a machine learning technique that might be able to learn the necessary polarity information just based on the words in the training data, without consulting a dictionary?


We evaluate on all opinion expressions that are known to have high level of inter-annotator agreement.

we include opinions with intensity marked as 'medium' or higher, and exclude those with annotation confidence marked as 'uncertain'.


In this paper, we present a novel lexicon adaptation technique based on integer linear programming to reflect the characteristics of the domain more directly.

our method collectively considers the relations among words and opinion expressions to derive the most likely polarity of each lexical item for the given domain.


With α < 1, f R (w) is effectively a dampened version of Pe(w) P (w) .

differences between very frequent words and very infrequent words are somewhat smoothed out.


useful can be classes which capture generalizations over a range of (cross-)linguistic properties, such as the ones proposed by Levin (1993).


Extending the approach is straightforward, for example, substituting more sophisticated data structures or statistical tests for the current ones.

we will move from the use of document boundaries to a flexible notion of textual distance to estimate likelihood of morphological relatedness.


While they do not necessarily achieve the same level of performance, they are appealing as unlabeled data is usually abundant.

for the purpose of exploring new domains and languages, obtainining labeled material can be prohibitively expensive and unsupervised learning methods are a very attractive choice.


As we show below, this model is flexible in the number of hidden states which it can accomodate.

the capacity is an uncertain quantity with an a priori infinite range that is a posteriori inferred by the data.


Also, it is commonly known that the parameter of a Dirichlet distribution controls how sparse its samples are.

by making the hyperprior on the Dirichlet distribution for the rows of the transition matrix small, we can encode our belief that any state (corresponding to a PoS tag in this application context) will only be followed by a small number of other states.


The following equation illustrates this idea: conditioned on the previous state s t−1 , the marginal probability of observation y t can be written as: The variable s t−1 = k specifies the mixing weights π k,• for the mixture distribution, while s t indexes the mixture component generating the observation y t .

equation 1says that each row of the transition matrix π specifies a different mixture distribution over the same set of K mixture components φ.


Thanks to the nice properties of kernel-based learning method and SVM classifier, we propose leveraging on the kernelized SVM learning algorithm to address the problem.

we propose using convolution tree kernel to capture the structured features and design a composite kernel to combine the structured features and other linear features for phrase reordering.


Naturally, as with any method, the proposed technique has certain limitations.

it is only concerned with influencing the translation rule probabilities via the corpus weights; it does not change the set of rules extracted.


In this section we describe the setup that was used for all experiments reported in this paper.

we provide details about the training data, development sets, and MT systems (phrase-based and hierarchical).


The development sets used for tuning and testing the corpus weights and other MT settings were comprised of documents from previous Arabic-English NIST MT evaluation sets and from GALE development/evaluation sets.

the newswire Tune and Test sets consist of documents from the following collections: the newswire portion of NIST MT04, MT05, MT06, and MT08 evaluation sets, the GALE Phase 1 (P1) and Phase 2 (P2) evaluation sets, and the GALE P2 and P3 development sets.


Therefore: where d etree,ftree is a derivation tree of rules that generates e tree and f tree .

e, the highest probability translation of f , can be approximated by taking the yield of the highest probability tree e tree that is a translation of the highest probability tree of f .


New Methods: The hybrid method variants proposed in this paper (hybrid-prop-cos-ll and hybrid-filt-cos-ll) were the best performers on the RG-65 test set.

they performed better than both the traditional word-distance measures (word-cos-ll), and our concept-based methodsvariants of the MH06 method that are used with likelihood ratios (concept-cos-ll, concept*-cos-ll).


Therefore, another focus of this paper will be to apply SRL to the RTE task.

it can improve the first stage binary classification (K vs. U), and the final result improves as well.


As a specific instantiation of our learning framework, the term-weighting function used in this paper is a linear combination of features extracted from the input term and document.

the weight of term t i with respect to document D p is where φ j is the j-th feature function and w j is the corresponding model parameter.


Our target application is query suggestion -automatically presenting queries that are related to the one issued by the user.

we would like to use our similarity measure as a filter to determine whether queries suggested by various algorithms and heuristics are indeed closely related to the target query.


• Meta: Besides Title, several meta tags used in the HTML header explicitly show the important words selected by the page author.

whether the term is part of a meta-keyword is used as a binary feature.


The proposed relational model of semantic similarity differs from feature models of similarity, such as the contrast model (Tversky, 1977), in that it is defined over the set of semantic relations that exist between two words instead of the set of features for each word.

in contrast model, the similarity S(a, b) between two objects a and b is defined in terms of the features common to a and b, A ∩ B, the features that are distinctive to a, A − B, and the features that are distinctive to b, B − A.


We use a modified version of the prefixspan algorithm (Pei et al., 2004) to generate subsequences from a text snippet.

we use the constraints (ii)-(iv) to prune the search space of candidate subsequences.


We propose quasisynchronous grammar (QG) features for these structured learning tasks.

we score a aligned pair of source and target trees based on local features of the trees and the alignment.


In the German-English translation pair, with co-indexed words aligned, we would prefer that the preposition an attach to denken, even though the preposition's object Libanon aligns to a direct child of remember.

we would like the grandparentparent-child chain of denken → an → Libanon to align to the parent-child pair of remember → Lebanon.


1 Given that these languages are inherently different from English and from one another, it appears that we cannot avoid a question concerning the adequacy of the models used to parse them.

given the properties of a language, which modeling strategy would be appropriate for parsing it?


Firstly, RR models significantly outperform HD models (about 2 points absolute improvement in F 1 ) in parsing the Modern Hebrew treebank.

rr models show better performance in identifying the constituents for which syntactic positions are relatively free.


S level elements, when embedded, always appear immediately after a conjunction or a relativizer.

nPs and PPs realize arguments and adjuncts that may occupy different positions relative to the head.


In the future we plan to investigate how the different models fare against one another in parsing different languages.

we wish to examine whether parsing different languages should be pursued by different models, or whether the RR strategy can effectively cope with different languages types.


Similarly, translation pairs selected during only one direction were less accurate.

translation pairs whose target language transla-tion was selected as a good translation for the source language entry; and whose source language translation was also selected as a good translation for the target language entry, should be awarded with a higher score.


Here, we allow not only instance-level inductive implications, but utilize it to get higher-level inductive implications.

we alter similarity space so that it can detect other clusters by changing the topology of the original space.


For y x ∈ y we propagate Set A yxv j = A v j yx (Algorithm 2, line 5-6).

we propagate the similarity between document v i and y nearest neighbors line 10-11).


In other words, we propagate the similarity between document v i and y nearest neighbors line 10-11).

we propagate the similarity between document v j and z nearest neighbors of v j to document v i .


In order to simulate this scenario we create a set of variations of the EuroParl corpus by treating some documents as if they have no parallel/comparable texts -i.e., we put each of these documents in a single-document tuple.

to do this, we divide the corpus W into two sets of document tuples: a "glue" set G and a "separate" set S such that |G| / |W| = p. the proportion of tuples in the corpus that are treated as "glue" (i.e., placed in G) is p. For every tuple in S, we assign each document in that tuple to a new singledocument tuple.


The rank correlation coefficient between total error rates on the two data sets was high (τ = 0.92; p < 5 × 10 −6 ).

if one system performs better than another on our artificial spelling errors, then the first system is very likely to also perform better on real typing errors.


We denote by q w the quantile function of the cdf corresponding to the acquisition distribution p w .

q w (r) represents the age at which r percent of the population T have acquired word w. Despite the fact that it does not have a closed form, it is a continuous and smooth function of the parameters µ w , σ w in (1) (assuming T is infinite) and can be tabulated before inference begins.


Following Definition 1 we define a logistic Rasch readability model: where An equivalent formulation to (3) that makes the probability model more explicit is .

4the probability of a document d being (s, r)-readable increases exponentially with q d (s, r) which is the age at which s percent of the words in d have been acquired each by r percent of the population.


Approximately 30 years have passed since Gilhooly and Logie's study was conducted.

society has made efforts to enhance the safety and health of children and to increase the attention to science education in very early grades.


Once acquisition age distributions are available, whether estimated statistically from data or obtained from a survey, they may be used to predict the grade level of novel documents.

the model predicts readability level t * for a novel document d if it is the minimal grade for which readability is established: where β(t) is a parameter describing the strictness of the readability requirement.


We do so by applying other predictive algorithms using the inferred acquisition age distribution for each document as the predictor variables and comparing the MAE with the MAE obtained by the estimated Rasch model.

we examine the performance of support vector regression (SVR) using the estimated AoA percentiles for each document as predictor variables.


Many language processing tools stand to benefit from better knowledge of this message structure, facilitating focus on relevant content in specific parts of a message.

access to zone information would allow email classification, summarisation and analysis tools to separate or filter out 'noise' and focus on the content in specific zones of a message that are relevant to the application at hand.


To identify zone boundaries in unannotated email data, we employ a very simple heuristic approach.

we consider every line in the body of an email message that matches any of the following criteria to be a zone boundary: 1.


The results of the k similar words method are far better than those of the other baseline methods.

the k similar words method with CVD outperformed the methods of the k similar words where the parameter value d was set to 0 and the method using RVD except for the top 1,000 results.


From a comparison of these relations, we found only 57 common hyponymy relations.

the remaining 243 hyponymy relations were not included in the possible hyponymy relations.


Its worst case time complexity is O(ncv) where n is the number of terms to be compared, c is the maximum number of non-zero coordinates of any vector, and v is the number of vectors that have a nonzero i th coordinate where i is the coordinate which is non-zero for the most vectors.

the algorithm is efficient only when the density of the coordinates is low.


The first stage involves a series of repeated replacements of SeedTerm2 by newly-learned terms in order to generate even more seed terms.

each new basic-level concept or instance is rotated into the pattern (becoming a new SeedTerm2) in a bootstrapping cycle that Kozareva et al.


In this section we evaluate the classification (taxonomy) that is learned by evaluating the links between the intermediate concepts and the basic-level concept/instance terms.

when our algorithm claims that isa(X,Y), how often is X truly a subconcept of Y?


World Model Freecell solitaire can be described formally using first order logic; we consider a slightly modified version of the representation from the Planning Domain Definition Language (PDDL), which is used in automatic game-playing competitions.

there are 87 constants: 52 cards, 16 locations, 13 values, four suits, and two colors.


First, since each candidate antecedent for a mention to be resolved (henceforth an active mention) is considered independently of the others, these models only determine how good a candidate antecedent is relative to the active mention, but not how good a candidate antecedent is relative to other candidates.

they fail to answer the critical question of which candidate antecedent is most probable.


Motivated in part by these recently developed models, we propose in this paper a clusterranking approach to coreference resolution that combines the strengths of mention-ranking mod-els and entity-mention models.

we recast coreference as the problem of determining which of a set of preceding coreference clusters is the best to link to an active mention using a learned cluster ranker.


While machine learning approaches to coreference resolution have received a lot of attention since the mid-90s, popular learning-based coreference frameworks such as the mention-pair model are arguably rather unsatisfactory from a linguistic point of view.

they have not leveraged advances in discourse-based anaphora resolution research in the 70s and 80s.


By contrast, our resolution strategy is learned without applying hand-coded con-straints in a separate filtering step.

we attempt to determine the compatibility between a cluster and an active mention, using factors that determine not only salience (e.g., the distance between the cluster and the mention) but also lexical and grammatical compatibility, for instance.


The feature set used to represent the instance is primarily composed of features that describe the relationship between m j and m k , as well as a few cluster-level features.

the model still relies heavily on features used in a mention-pair model.


In other words, the model still relies heavily on features used in a mention-pair model.

the inclusion of m j in the feature vector representation to some extent reflects the authors' lack of confidence that a strong entity-mention model can be trained without mention-pair-based features.


After training, the resulting SVM classifier is used to identify an antecedent for a mention in a test text.

an active mention m k selects as its antecedent the closest preceding mention that is classified as coreferent with m k .


Motivated by previous work Culotta et al., 2007;, we create cluster-level features from mention-pair features using four predicates: NONE, MOST-FALSE, MOST-TRUE, and ALL.

for each feature X shown in the last two blocks in Table 1, we first convert X into an equivalent set of binary-valued features if it is multi-valued.


After training, the resulting classifier is used to identify a preceding cluster for a mention in a test text.

the mentions are processed in a left-to-right manner.


Since a cluster ranker is a hybrid of a mention-ranking model and an entity-mention model, the way it is trained and applied is also a hybrid of the two.

the instance representation employed by a cluster ranker is identical to that used by an entity-mention model, where each training instance i(c j , m k ) represents a preceding cluster c j and a discourse-old mention m k and consists of cluster-level features formed from predicates.


Unlike in an entity-mention model, however, in a cluster ranker, (1) a training instance is created between each discourse-old mention m k and each of its preceding clusters; and (2) since we are training a model for ranking clusters, the assignment of class values to training instances is similar to that of a mention ranker.

the class value of a training instance i(c j , m k ) created for m k is the rank of c j among the competing clusters, which is 2 if m k belongs to c j , and 1 otherwise.


Applying the learned cluster ranker to a test text is similar to applying a mention ranker.


Hence, to jointly learn discourse-new detection and coreference resolution, we must train the ranker using instances generated from both discourse-old and discourse-new mentions.

when training the ranker, we provide each active mention with the option to start a new cluster by creating an additional instance that (1) contains features that solely describe the active mention (i.e., the features shown in the second block of Table 1), and (2) has the highest rank value among competing clusters (i.e., 2) if it is discourse-new and the lowest rank value (i.e., 1) otherwise.


This paper employs a global learning method in determining the anaphoricity of noun phrases via a label propagation algorithm to improve learningbased coreference resolution.


Compared with previous methods, the LP algorithm can effectively capture the natural clustering structure in both the labeled and unlabeled data to smooth the labeling function.


On average, an anaphor has ~7 antecedent candidates.

the test corpus is resolved in document-level, i.e.


This paper systematically studies a global learning method in identifying the anaphoricity of noun phrases via a label propagation algorithm and the application of an explicit anaphoricity determination module in improving learningbased coreference resolution.


Moreover, given a pronominal anaphor, its relative ranking among all the pronouns in a sentence, according to relevant semantic role information and its surface position, is incorporated.

the use of both the semantic role features and the relative pronominal ranking feature in pronoun resolution is guided by extending the centering theory from the grammatical level to the semantic level in tracking the local discourse focus.


For example, Yang et al (2005) proposed a template-based statistical approach to compute the semantic compatibility between a pronominal anaphor and an antecedent candidate, and Yang and Su (2007) explored semantic relatedness information from automatically discovered patterns, while Ng (2007) automatically induced semantic class knowledge from a treebank and explored its application in coreference resolution.

this paper focuses on the centering theory (Sidner,1981;Grosz et al.,1995;Tetreault,2001), which reveals the significant impact of the local focus on referring expressions in that the antecedent of a referring expression usually depends on the center of attention throughout the local discourse segment (Mitkov,1998).


(1987) presented a centering theory-based formalism in modeling the local focus structure in discourse and used it to track the discourse context in binding occurring pronouns to corresponding entities.

a BFP (Brennan, Friedman and Pollard) algorithm is proposed to extend the original centering model to include two additional transitions called smooth shift and rough shift.


2) The backward-looking center C b of U n depends only on the expressions that constitute the utterance.

it is independent of its surface position and grammatical roles.


Among them, named entity recognition, part-of-speech tagging and noun phrase chunking apply the same Hidden Markov Model (HMM)-based engine with error-driven learning capability Su, 2000 &2002).

for SRL, we use a state-of-the-art inhouse toolkit, which achieved the precision of 87.07% for ARG0 identification and the precision of 78.97% for ARG1 identification, for easy integration.


reflexive) can be ignored due to their much less frequent occur-rence in the corpus.

the centeringmotivated features improve the performance on the major three pronoun subcategories of third person / neuter / first person, by 8.3(>>>)/6.8(>>>)/9.0(>>>), 0.9(>>)/ 2.6 (>>>)/11.1(>>>) and 4.7(>>>)/8.1(>>>)/9.0 (>>>), on the NWIRE, NPAPER and BNEWS domains of the ACE 2003 corpus, respectively.


For future work, we will explore more kinds of semantic information and structured syntactic information in pronoun resolution.

we will further employ the centering theory in pronoun resolution from both grammatical and semantic perspectives on more corpora.


We can compute the probability that a certain percentage of the names within that particular partition class have a given name perplexity.

we consider a random sample inside the partition class, X, and its correspondent random variable Y, as above.


This restriction turns out to improve performance slightly, even though it reduces the quality of our approximation to the LOP problem (4).

it turns out that reorderings found outside the ITG neighborhood tend to be poor German even if our LOP-based objective function thinks that they are good German .


We used the resulting German as the input to the Moses training pipeline.

moses recomputed alignments of the German training data to the English sentences using GIZA++, then constructed a phrase table.


(2003), we estimate the two distributions by relative frequencies from the training corpus annotated with weighted alignment matrices.


However, in their work, both rule application and the semantics of the resulting "augmented" structure were not fully specified.

the distinction between individual consequents was lost in the augmented graph.


We expect these frequency indicators to be superior to a word-level unigram model, as they allow the designer to select notable frequencies.

the bins we have selected do not give any advantage to extremely common words, as these are generally less likely to be transliterated.


We experimented with different approaches for training the discriminative models and two different corpora: the french MEDIA corpus (Bonneau-Maynard et al., 2005) and a corpus made available by the European project LUNA 1 (Dinarelli et al., 2009b).

the new contents with respect to our previous work (Dinarelli et al., 2009a) are: • We designed a new sequential structure (SK2) and two new hierarchical tree structures (MULTILEVEL and FEATURES) for re-ranking models (see Section 4.2).


b) The quality of dedicated click data may be not as reliable as human labeled training data.

there are some extracted click preference pairs that are inconsistent with human labeling while we regard human labeling as correct labeling.


In this paper, we address the problem of building an ensemble of diversified machine translation systems from a single translation engine for system combination.

we propose a novel Feature Subspace method for the ensemble construction based on any baseline SMT model which can be formulated as a standard linear function.


In order to decide the sequence of sub-systems to add, we first evaluate the performance of pair-wise combinations between each sub-system and its baseline system on the development set.

for each sub-system, we combine its n-best list with the n-best list of its baseline system and perform system combination for MT03 data set.


Relation to L2 SVM The classic soft-margin SVM formulation uses L1-penalty for misclassified instances.

the objective of the learner is to minimize 1 2 ||w|| 2 + C i ξ i subject to some margin constraints, where w is a weight vector to be learned and ξ i is the misclassification error for instance i.


However, for the case of non-linear kernels, anchored and L2-SVM produce different results, as the anchoring is applied prior to the kernel expansion.

for the case of the second-degree polynomial kernel, L2-SVM aims to maximize: In our experiments, as discussed in Section 5.4, we find that anchored-SVM and soft-margin SVM with tuned C value both reach good results when we reduce the amount of lexical features.


Instead, we consider a vastly more modular system in which coreference is predicted from a deterministic function of a few rich features.


There are however wordpairs which introduce errors.


The most errors are made on nominal mentions with pronouns coming in a distant second.

we most frequently say a nominal is NULL when it has an antecedent; this is typically due to not having the necessary semantic knowledge to link a nominal to a prior expression.


Thus, instead of a direct estimation, we substitute Score base (l, r) as defined above.

we assume Actually, Score base originally had another motivation, inspired by Torisawa (2005), for which no postposition but the instrumental postposition de was relevant.


iv) Retrieve N-best verb pairs as the final output from the result of iii).

we first acquired unary template pairs and then transformed them into verb pairs.


Each Lin pair was regarded as correct if either direction was judged correct.

we evaluated the upper bound performance of the LEDIR algorithm.


If tr xy () is a translation function that maps a concept c i ∈ C x into the concept c i ∈ C y via the interlanguage links, we can write: The projection of the ESA vector t from language x onto y can be written as: Using equations 5, 7, and 9, we can calculate the cross-lingual semantic relatedness between any two content terms a x and b y in given languages x and y as: Note that the weights assigned to Wikipedia concepts inside the concept vectors are language specific.

two Wikipedia concepts from different languages, mapped via an interlanguage link, can, and often do have different weights.


Unlike the previous experiment, this time we can achieve full translation coverage, and thus we are able to produce data sets of equal size that can be used for a comparison between relatedness measures.

using the translation produced by the machine translation engine for the first word in a pair, we calculate the relatedness within the space of the language of the second word using a monolingual ESA also based on Wikipedia.


In this work we choose shift-reduce dependency parsing for its simplicity and efficiency.

we make the following contributions: • we develop a baseline shift-reduce dependency parser using the less popular, but classical, "arc-standard" style (Section 2), and achieve similar state-of-the-art performance with the the dominant but complicated "arceager" style of Nivre and Scholz (2004); • we propose bilingual features based on wordalignment information to prefer "target-side contiguity" in resolving shift-reduce conflicts (Section 3); • we verify empirically that shift-reduce conflicts are the major source of errors, and correct shift-reduce decisions strongly correlate with the above bilingual contiguity conditions even with automatic alignments (Section 5.3); • finally, with just three bilingual features, we improve dependency parsing accuracy by 0.6% for both English and Chinese over the state-of-the-art baseline with negligible (∼6%) efficiency overhead (Section 5.4).


We have presented a novel parsing paradigm, bilingually-constrained monolingual parsing, which is much simpler than joint (bi-)parsing, yet still yields mild improvements in parsing accuracy in our preliminary experiments.

we showed a simple method of incorporating alignment features as soft evidence on top of a state-of-the-art shift-reduce dependency parser, which helped better resolve shift-reduce conflicts with fractional efficiency overhead.


Note that the training and development datasets have exactly the same distributions of SCs due to the stratification procedure.

each class has the same proportion in training and development datasets.


The first experiment evaluates the individual contribution of different feature sets to SC classification accuracy.

a ME model is trained on the 55,629 training instances using the following feature sets separately: 1) unigram, 2) bigram, 3) first-last word, 4) head word (HW), 5) named entities (NE), 6) HW+WN CLASS, 7) HW+WN SIM, and 8) variants of HW+WN HYP.


5 Finally, we perform the feature ablation experiments.

we remove one feature at a time from the entire feature set and test the accuracy loss.


We then use these two induced la-bels to propose the SC agreement feature for N P i and N P j .

sC sTRICT is true if l i and l j are the same and they are not of other type, false otherwise; sC COARsE is true if both l i and l j are not of other type; In addition, we propose two other sC agreement features to cope with the sC classification errors.


Malapropisms were randomly induced into this text at a frequency of approximately one word in 200.

any word whose base form was listed as a noun in WordNet (but regardless of whether it was used as a noun in the text; there was no syntactic analysis) was potentially replaced by any spelling variation found in the lexicon of the ispell spelling checker 7 .


To gain insight into our work, it is useful to consider that E2, 3 and F1, 4, 6 are summaries of two discourses.

f1 denotes a question and f4 and f6 are corresponding answers.


Using the same derivation in Section 4.2, we obtain the following gain function and update rules: Intuitively, an unlabeled example that has a high-confidence |F (x)| at the current step, will probably receive more weight at the next step.

similar instances become more important when learning and mining subtrees.


To verify our claim, we evaluated the supervised speech act recognition on EMAIL and FORUM evaluation sets with 5-fold cross validation (SUPERVISED in Table 4).

our semi-supervised speech act recognition is competitive with the supervised model in FORUM data.


The labeling of each character considers both the previous character's features and the next character's features.

if the current character is the first character, its previous character is an empty character (which is used for segmenting sequences in CRF); if the current character is the second character, its next character is an empty character.


Experiments show that adopting morphological types annotated by two classifiers for polarity prediction has little difference.

cRF and SVM classifiers have an 8% f-score difference in their best performance of classification, while the performance gap in word polarity prediction using morphological types provided by these two classifiers is around 1.3% only (0.5806 vs. 0.5938).


Due to the difficulty in deep semantic parsing, most of previous work focuses on shallow semantic parsing, which assigns a simple structure (such as WHO did WHAT to WHOM, WHEN, WHERE, WHY, HOW) to each predicate in a sentence.

the well-defined semantic role labeling (SRL) task has been drawing more and more attention in recent years due to its importance in deep NLP applications, such as question answering (Narayanan and Harabagiu, 2004), information extraction (Surdeanu et al., 2003), and co-reference resolution (Ponzetto and Strube, 2006).


Among them, the predicate class (b2) feature was first introduced in Xue and  to overcome the imbalance of the predicate distribution in that some predicates can be only found in the training data while some predicates in the testing data are absent from the training data.

the verb class is classified along three dimensions: the number of arguments, the number of framesets and selected syntactic alternations.


In this paper we investigate nominal SRL in Chinese language.

some nominal SRL-specific features are included to improve


We apply our model to the CLASSIFIEDS data (Grenager et al., 2005).

we use VE to incorporate into a CRF model two types of prior knowledge specified in previous works.


To optimize such an objective, we apply the EM algorithm in the same fashion as is used in a generative approach.

we iteratively optimize Q(λ) = y p λ g (y|x, v) log p λ (y, v|x) where λ g denotes the model estimated from the previous iteration.


To maximally reduce ambiguity, we propose to apply s 1 in Equation (9) if both of the following conditions hold, (1) x t ∈ C l (2) There exists τ s.t.

|τ − t| < k, and x τ ∈ C l we will impose a non-uniform prior on y t if x t ∈ C l "collocates", within k tokens, with another word that belongs to C l .


Similar to the second decoding setting that incorporates VE, we can have a counterpart setting at training time.

we can optimize p λ (y|x, v) instead of p λ (y|x) during learning.


Starting with the basic nonterminals, this method repeats the split-merge (SM) cycle to increase the complexity of grammars.

it splits every symbol into two, and then re-merges some new subcategories based on the likelihood computation.


Even though the big TTS templates still carry the original alignment information, they have much less chance of getting matched beyond the syntax tree where they were extracted, as we show in Section 4.

a few cross-boundary noisy alignments could disable a big portion of a training syntax tree, while for a phrase-based SMT system, their effect is limited to the phrases they align.


In fact, a considerable number of works on semantic processing implicitly or explicitly presupposes the availability of a lexicon, such as in word sense disambiguation (WSD) (Mc-Carthy et al., 2004), and in token-level verb class disambiguation (Lapata and Brew, 2004;Girju et al., 2005;Li and Brew, 2007;Abend et al., 2008).

those methods are heavily dependent on the availability of a semantic lexicon.


The principal idea behind the work is that the meanings of verbs can be identified by observing possible syntactic frames that the verbs can take.

with the knowledge of syntactic frames, verbs can be semantically classified.


The extensive research is in large part due to the intuition that the set of classes could not be fixed beforehand.

it is often problematic to define a static set of semantic classes.


With this assumption, probabilistic modeling can be reduced to a classification problem.

the goal is to obtain a probability distribution, p(c|v), of verb class c ∈ C for a given verb (lemma) v. We can therefore apply well-known supervised learning methods to estimate p(c|v).


The results show that although some kernels are less effective than others, they exhibit properties that are complementary to each other.

we found that relation extraction can benefit from increasing the feature space by combining kernels (with a simple summation) exploiting the two different parsing paradigms.


However, the scope of the speculation keyword "suggesting" in the second sentence applies only to the second clause of the sentence.

only the statement "RAD9B is a RAD9 paralog that engages in similar biochemical reactions" is speculative.


However, these keywords are not always used in a speculative context.

they are not always real speculation keywords.


Unlike pervious studies, which treat the problem of identifying speculative language as a sentence classification task, we tackle the more challenging problem of identifying the portions of sentences which are speculative.

we allow a sentence to include both speculative and non-speculative parts.


The keywords were annotated with a minimalist strategy.

the minimal unit that expresses speculation was annotated as a keyword.


The bag-of-words features for the keyword "indicate", when a window size of three and no stemming is used are: "our", "findings", "may", "indicate", "the", "presence", "of".

the feature set consists of the keyword, the three words to the left of the keyword, and the three words to the right of the keyword.


The scope of the conjunction "or", extends to the "VP" whose children it coordinates.

the scope of "or" is "[might be one of the earliest crucial steps in the lysis of normal and dex-resistant CEM cells, or might serve as a marker for the process]".


The scope of both occurrences of the modal verb "might" is the parent "VP".

the scope of the first occurrence of "might" is "[might be one of the earliest crucial steps in the lysis of normal and dex-resistant CEM cells]" and the scope of the second occurrence of "might" is "[might serve as a marker for the process]".


While the two datasets consist of mostly different topics, there are a few that would be interesting to compare.

we examine the topic of food and eating.


To see where these perspectives on food differ the most, we computed the ratio of the probability of each word given the topic between the two datasets.

if p = P (w|z) in the locals data and q = P (w|z) in the tourists data, then λ = p/q gives us a measure of how much more (or less) prominent that word is among locals than it  shoes  fashion  price  garden  water  cat  church  krishna  god  fashion  women  posted  dog  energy  cats  god  religion  sin  clothing  indian  earrings  pet  carbon  dog  john  religious  john  high  designer  length  cat  earth  pet  todd  spiritual  spirit  designer  sarees  item  dogs  green  training  bentley  guru  things  style  leather  sgd  pets  solar  pets  jesus  lord  lamb  love  girls  silver  gardening  jai  hamster  christ  sri  exodus  london  china  clothes  cats  climate  cute  luke  shri  suffering  shirts  jewellery  shop  puppy  environment  hamsters  bible  baba  cross  bag  jewelry  code  flowers  warming  rabbit  christian  hindu  lives   Table 2: A sample of topics induced on a set of blogs from 3 countries.


We further assume that these features decompose over hyperedges in the hypergraph, like the one in Figure 3.

in this setting, we can analytically compute the gradient of CoBLEU.


We extract a k-best list from the forest, then select the translation that yields the highest BLEU score relative to the forest's expected n-gram counts.

let BLEU(e; r) compute the similarity of a sentence e to a reference r based on the n-gram counts of each.


We classify a new query q according to U RL * , the neighboring URL with the strongest opinion (highest absolute value of the log likelihood ratio).

we compute U RL * with: If the neighboring opinion is positive (that is, Pr(Intent|U RL * ) > Pr(¬Intent|U RL * )), then the query q is assigned a positive label.


With the plenitude of unlabeled natural language data at hand, semi-supervised learning can significantly reduce the need for labeled data with only limited sacrifice in performance.

a bootstrapping algorithm chooses the unlabeled instances with the highest probability of being correctly labeled and use them to augment labeled training data iteratively.


In this paper, we select Support Vector Machines (SVMs) as the underlying supervised classifier since it represents the state-of-the-art in the machine learning research community, and there are good implementations of the algorithm available.

we use LIBSVM (Chang et al., 2001), an effective tool for support vector classification, since it supports multi-class classification and provides probability estimation as well.


This section aims to empirically investigate the effectiveness of the bootstrapping-based semisupervised learning we discussed above for semantic relation classification.

different methods for selecting the initial seed set and augmenting the labeled data are evaluated.


For easy reference with related work in the literature, evaluation is also done on 347 documents (including nwire and bnews domains) and 4305 relation instances using 5-fold cross-validation.

these relation instances are first divided into 5 sets, then, one of them (about 860 instances) is used as the test data set, while the others are regarded as the training data set, from which the initial seed set is sampled.


For each iteration, a variable number (m is ranged from 100 to 500) of classified instances in the decreasing order of confidence are first chosen as the base examples, then at most 100 examples are selected from the base examples to be augmented to the labeled set.

when m is equal to 100, the whole set of the base example is added to the labeled data, i.e.


These two figures jointly show that:  The performance decreases slowly while the bootstrapping time decreases dramatically with the increase of p from 0 to 0.3.

when the p equals to 0.3, the bootstrapping time tends to be neglected, while the performance is almost similar to the initial performance.


When one mention of an entity is tagged any category or recognized as an NE, our model also uses the category or the NE class as the content part of the entity.

for estimating Equation (iii), our model also calculates: P (NE :ARTIFACT |kakaku(1), no, A (no)=1) P (NE : ARTIFACT ) besides: and uses the geometric mean of them.


Syntactically, an MLN consists of a set of firstorder logical formulas F and a real-valued weight w F for each F ∈ F. Semantically, an MLN defines a probability distribution over possible groundings of the logical formulas.

if U denotes the set of all objects in the universe, and G denotes the set of all possible ways to ground every F ∈ F (i.e., substitute an element from U for every variable in F ), then an MLN defines a distribution over truth assignments to the grounded formulas G ∈ G. Let I denote the set of all possible interpretations of G -that is, each I ∈ I assigns true or false to every G ∈ G. The probability of a particular interpretation I according to the MLN is given by : where n(F, I) gives the number of groundings of F that are true in interpretation I.


But the performance decline slows up when the length of arguments is larger than 10.

some of the arguments that are composed of many words can still be rightly identified.


In information retrieval, the problem of vocabulary mismatch between the query and the terms in the document has been addressed in many ways, as mentioned in Section 1, achieving varying degrees of success in the retrieval task.

our work is closely related to research in spelling correction for English web queries (e.g., Cucerzan and Brill, 2004;Ahmad and Kondrak, 2005;Li et al., 2006;Chen et al., 2007).


Finally, the character type features also contributed to reducing the error rate (lines A and E).

the observation that the addition of semantic features without the character type features (comparing lines B and E) did not improve the error rate indicates that the character type features are also important in bringing about the contribution of semantic features.


For instance, in chemistry there is a niche for search applications which guide searchers directly to the method and/or result sections in papers.

the OWN FAIL category is motivated by the failure-and-recovery search.


1 κ is also designed to abstract over the number of annotators as its formula relies on the proportion of expected vs. observed pairwise agreements possible in a pool.

κ for k annotators will be an average of the values of κ taking all possible m-tuples of annotators from the annotator pool (with m < k).


A 1 Artstein and Poesio (2008) observe that there are several version of κ which differ in how P (E) is calculated.

fleiss' (1971) κ calculates P (E) as the average observed distribution of all annotators, whereas Cohen's (1960) κ calculates P (E) only on the basis of the other annotator(s).


Extracting relations at character-level from semi-structured documents has been proposed (e.g., (Kushmerick et al., 1997), (Brin, 1998)).

brin's approach (DIPRE) is the most similar to ours in terms of expanding relational items.


Nevertheless, when |D| is large (e.g., > 100), the performance of classification may decrease, especially when the distribution of d in training dataset differs from that in the test set.

when |D| is large, named entity disambiguation becomes a multi-class classification task on heterogeneous and imbalanced datasets, which is challenging for a machine learning model to learn to discriminate enough between the semantic classes (Japkowicz, 2000).


Among the SVM-based systems, the one using IG feature selection achieved better performance.

it outperformed the Maxent model in term of macro-averages.


Below, we focus on a kernel-based classifier trained with a polynomial kernel of degree d (here, 1 For example, given an input vector x = (x1, x2) T and a support vector In the following argument, we ignore the dimension of the constant in the mapped space and assume constant b is set to include it.

2 speaking, they set different thresholds to positive (αj > 0) and negative (αj < 0) support vectors, considering the proportion of positive and negative support vectors.


The proposed framework leverages on the strengths of previous system combination and reranking methods and is open to any type of features.

it is capable of utilizing the logarithm of the parse tree probability from each individual parser while previous combination methods are unable to use this feature since the probabilities from different parsers are not comparable.


The intuition behind this method is the hidden structural information among the documents: similar documents are likely to have the same hidden information with respect to a query.

if a group of documents are talking about the same topic which shares a strong similarity with a query, in our method they will get allocated similar ranking as they are more likely to be relevant to the query.


Given two probability mass function and ( ), the KL divergence (or relative entropy) between and is defined as: In terms of text sequences (either queries or documents), the probability distribution can be regarded as a probabilistic language model or from each document or each query .

it assumes that there is an underlying language model which "generates" a term (sequence) (Ponte and Croft, 1998).


The regular product model improves upon its individual grammars more than the ST-Prod-Mult product does in the later SM rounds, as illustrated by the relative error reduction curves in figures 2(a) and (b).

the product of the SM7 regular grammars gains a remarkable 2.1% absolute improvement over the average performance of the individual regular SM7 grammars and 0.2% absolute over the product of the regular SM6 grammars, despite the fact that the individual regular SM7 grammars perform worse than the SM6 grammars.


We use the average empirical variance of the log posterior probabilities of the rules among the learned grammars over a held-out set S as a proxy of the diversity among the grammars: where R(G, s) represents the set of rules extracted from the chart when parsing sentence s with grammar G, and VAR(log(p(r|s, G))) is the variance of log(p(r|s, G)) among all grammars G ∈ G. Note that the average empirical variance is only an approximation of the diversity among grammars.

this measure tends to be biased to produce larger numbers when the posterior probabilities of rules tend to be small, because small differences in probability produce large changes in the log scale.


This measure originates in work on lexical priming (Church, 2000), and quantifies the probability of a target word or construction w appearing in a "primed" context.

four frequencies are calculated, based on whether the target construction appears in the previous context (the prime set), and whether the construction appears after this context (the target set): f p,¬t (w) = # of times w in prime set only f ¬p,t (w) = # of times w in target set only f ¬p,¬t (w) = # of times w in neither set f p,t (w) = # of times w in both sets We also define N to be the sum of the four fre-LHS prior pos adapt ratio + > prior sig.


However, we found that our most successful feature function is to consider overlaps in partial structures of productions.

we decompose a tree into all of the nonlexical vertically and horizontally markovized subtrees.


By analyzing the features and feature weight values extracted from the parse sequence, we determined that the context-aware reranker is able to correct the analysis of the scoping due to a parallelism in the syntactic structure.

the substructure S → V P. is present in both this sentence and the previous sentence of the reranked sequence, which also contains a reporting verb.


We should thus consider integrating both types of features into the reranking parser to build on the advantages of each.

withinsentence features are most appropriate for lexical dependencies and some structural dependencies.


We will view sentiment analysis as a regression problem: given an input document, we want to predict a real-valued observation y that represents the sentiment of a document.

we build on supervised latent Dirichlet allocation (SLDA, (Blei and McAuliffe, 2007)), which makes predictions based on the topics expressed in a document; this can be thought of projecting the words in a document to low dimensional space of dimension equal to the number of topics.


A wide spectrum of tasks have been studied under review mining, ranging from coarse-grained document-level polarity classification (Pang et al., 2002) to fine-grained extraction of opinion expressions and their targets (Wu et al., 2009).

a general framework of summarizing reviews of a certain product is to first identify different aspects (a.k.a.


Our model is simpler and yet still produces meaningful aspects.

we assume that there are T aspects in a given collection of reviews from the same domain, and each review document contains a mixture of aspects.


Since LocLDA does not identify aspect-specific opinion words, we consider the following two baseline methods that can identify aspect-specific opinion words: BL-1: In this baseline, we start with all adjectives as candidate opinion words, and use mutual information (MI) to rank these candidates.

given an aspect t, we rank the candidate words according to the following scoring function: where V t is the set of the top-100 frequent aspect words from φ A,t .


Thus, given a set of opinionated documents about a topic, we aim at automatically extracting and summarizing the multiple contrastive viewpoints implicitly expressed in the opinionated text to facilitate digestion and comparison of different viewpoints.

we will generate two types of multiview summaries: macro multi-view summary and micro multi-view summary.


The second modification is to further favor jumping between two excerpts that can potentially form a good contrastive pair for use in generating a micro contrastive summary.

under our model, the random walker first decides whether to jump to a sentence of the same viewpoint or to a sentence of a different viewpoint.


We define the score for a pair (x i , x j ) as the probability of being at x i and transitioning to x j or vice versa, where x i and x j are of opposite viewpoints.

: The final summary should be a set of excerpts that have a high relevance score according to our scoring algorithm, but are not redundant among each other.


Some aspects of affect state identification are closely related to Hopper and Thompson's (1980) theory of transitivity.

their notions of aspect (has an action completed?


It consists of a group of over 15,000 specialists who systematically identify and review randomized trials of the effects of treatments.

a review contains a reference section, listing all the relevant studies to the clinical question.


The main idea behind our approach is that edges in the co-occurrence graph participating in cycles are likely to connect vertices (i.e., words) belonging to the same meaning component.

we focus on cycles of length 3 and 4, called respectively triangles and squares in graph theory.


While assessing the quality of clustering is a notably hard problem, given a gold standard G we can calculate the Rand index (RI) of a clustering C, a common quality measure in the literature, determined as follows (Rand, 1971;Manning et al., 2008): where W is the union set of all the words in C and δ(w, w ) = 1 if any two words w and w are in the same cluster both in C and in the gold standard G or they are in two different clusters in both C and G, otherwise δ(w, w ) = 0.

we calculate the percentage of word pairs that are in the same configuration in both C and G. For the gold standard G we use the clustering induced by the sense annotations provided in our datasets for each snippet (i.e., each cluster contains the snippets manually associated with a particular Wikipedia subtopic  Results.


Each synchronous production (i.e., rule) rewrites a nonterminal into a pair of strings, γ and α, where γ (or α) contains terminal and nonterminal symbols from the source (or target) language and there is a one-toone correspondence between the nonterminal symbols on both sides.

the hierarchical model (Chiang, 2007) studied in this paper explores hierarchical structures of natural language and utilize only a unified nonterminal symbol X in the grammar, where ∼ is the one-to-one correspondence between X's in γ and α, and it can be indicated by underscripted co-indexes.


Note that any sub-sequence of phrase pairs in a chain generates a tight phrase pair.

chain L generates a tight phrase pair τ (L) that corresponds exactly to the union of the alignment links in p ∈ L. We call the phrase pairs generated by maximal chains maximal phrase pairs and call the other phrase pairs non-maximal.


This motivates us to perform MERT at the wordtoken level, although our input consists of morphemes.

for each iteration of MERT, as soon as the decoder generates a morpheme-token translation for a sentence, we convert it into a wordtoken sequence, which is used to calculate BLEU.


The BLEU score impact on WMT has been fairly consistent during our experimentation: the BLEU score is roughly halved.

for the above system the baseline score is 35.33, and stress The proportion of sentences successfully matched is 85%, and if we permit a single stress error, it is 93%, which suggests that the constraint can be satisfied in the great majority of cases.


However, their approach is too demanding computationally (see Section 5), so instead we use local sequence contexts as graph vertices, exploting the empirical observation that the part of speech of a word occurrence is mostly determined by its local context.

the set V of graph vertices consists of all the word n-grams 1 (types) that have occurrences (tokens) in training sentences (labeled and unlabeled).


We expect the similarity graph to provide information that cannot be expressed directly in a sequence model.

it is not possible in a CRF to directly enforce the constraint that similar trigrams appearing in different sentences should have similar POS tags.


The second term is the graph smoothness regularizer which requires that the q i 's be smooth with respect to the graph.

if w uv is large, then q u and q v should be close in the squared-error sense.


In this paper, we tackle the task of predicting punctuation symbols from a standard text processing perspective, where only the speech texts are available, without relying on additional prosodic features such as pitch and pause duration.

we perform the punctuation prediction task on transcribed conversational speech texts, using the IWSLT corpus (Paul, 2009) as the evaluation data.


They adopted a HMM to describe a joint distribution over words and interword events, where the observations are the words, and the word/event pairs are encoded as hidden states.

in this task word boundaries and punctuation symbols are encoded as interword events.


Unlike a HMM which models the joint distribution of both the label sequence and the observation, a CRF is a discriminative model of the conditional distribution of the complete label sequence given the observation.

a first-order linear-chain CRF which assumes first-order Markov property is defined by the following equation: (1) where x is the observation and y is the label sequence.


We can model the punctuation prediction task as the process of assigning a tag to each word, where the set of possible tags is given in Table 1.

we assume each word can be associated with an event, which tells us which punctuation symbol (possibly NONE) should be inserted after the word.


However, the linearchain CRF model still exhibits several problems for the punctuation task.

the dependency between the punctuation symbols and the indicative words cannot be captured adequately, if they appear too far away from each other.


For the methods based on the hidden event language model, we design extensive experiments due 2 http://www.cis.upenn.edu/∼treebank/tokenization.html to many possible setups.

these experiments can be divided into two categories: with or without duplicating the ending punctuation symbol to the start of a sentence before training.


The  BTEC   NO DUPLICATION  USE DUPLICATION  SINGLE PASS  CASCADED  SINGLE PASS  CASCADED  L-CRF F-CRF  LM ORDER  3  5  3  5  3  5  3    performance of the hidden event language model heavily depends on whether the duplication method is used and on the actual language under consideration.

for English, duplicating the ending punctuation symbol to the start of a sentence before training is shown to be very helpful in improving the overall prediction performance.


This is because  lexicalized reordering gives better performance than simple distance-based reordering (Koehn et al., 2005).

the default lexicalized reordering model (msd-bidirectional-fe) is used.


Different from traditional joint approach, our method integrates two linear markov chains for segmentation and tagging subtasks into one that contains two types of nodes.

we first regard segmentation and tagging as two independent sequence labeling tasks, corresponding chain structures are built, as shown in the top and middle sub-figures of Figure 2.


Then neighboring tagging and segmentation states are connected as shown in the bottom sub-figure of Figure 2.

non-violation of hard-constraints that nodes within the same segment get a single consistent tagging label is guaranteed by introducing second order transition features it is true, if a segment is partially tagging as PER, and partially tagged as LOC.


In this work, we followed the supervised approach and proposed two novel techniques to improve the current state-of-the-art.

we proposed a new class of features which are POS sequence patterns that are able to capture complex stylistic regularities of male and female authors.


Several algorithms have been developed in the past few years that can learn from a set of labeled positive examples augmented with a set of unlabeled examples.

given a set P of positive examples of a particular class (called the positive class) and a set U of unlabeled examples (which contains both hidden positive and hidden negative examples), a classifier is built using P and U to classify the data in U as well as future test data into two classes, i.e., those belonging to P (positive) and those not belonging to P (negative).


The first step uses Rocchio classification (Rocchio, 1971) to find a set of reliable negatives RN from U.

this method treats the entire unlabeled set U as negative documents and then uses the positive set P and the unlabeled set U as the training data to build a Rocchio classifier.


In order to create different experimental settings, we vary the number of the other topic documents that are added to the test set as negatives, controlled by a parameter α, which is a percentage of |TN|, where |TN| is the size of the negative test set without the other topic documents.

the number of other topic documents added is α × |TN|.


A major weakness of many existing essay scoring engines such as IntelliMetric (Elliot, 2001) and Intelligent Essay Assessor (Landauer et al., 2003) is that they adopt a holistic scoring scheme, which summarizes the quality of an essay with a single score and thus provides very limited feedback to the writer.

it is not clear which dimension of an essay (e.g., coherence, relevance) a score should be attributed to.


As mentioned above, our first approach to heuristic organization scoring involves aligning paragraph sequences.


We solve this problem by heuristically defining the similarity function as follows: S(i, j) = 1 when i = j, S(i, j) = −1 when i = j, and also S(i, −) = S(−, i) = −1, where '−' is an indel.

the similarity function encourages the alignment between two identical function labels and discourages the alignment between two different function labels, regardless of the type of function labels.


Given a test essay e, we first find for each paragraph in e the k paragraphs in the training set that are most similar to it.

each paragraph is represented by its sequence of sentence function labels.


In our experiments, we select values for these parameters in a somewhat arbitrary manner.

since λ ranges between 0 and 1, we simply set it to 0.5.


If we want our learner to make use of more than one of these types of features, we need to employ a composite kernel to combine them.

we define and employ the following composite kernel: where F 1 and F 2 are the full set of features (containing both flat and structured features) that represent the two essays under consideration, K i is the ith kernel we are combining, and n is the number of kernels we are combining.


Next, we explore the impact of composite kernels, which allow our learners to make use of multiple types of flat and structured features.

the results shown in rows 7-9 are obtained by combining two kernels at a time.


These results show that topic quality on both the raw and pre-processed noisy data degrades at a rate relative to the amount of errors in the data.

the difference in performance between two relatively low word error rates (e.g.


The covariance matrix formed from differences between comparable documents is the noise covariance in OPCA: we wish to minimize the latter covariance, to make the projection languageindependent.

we create the weighted documentterm matrix D m for each language: We then derive a signal covariance matrix over all languages: where µ m is the mean of each D m over its columns, and a noise covariance matrix, where D is the mean across all languages of the document-term matrix, and M is the number of languages.


We use the log-likelihood according to this model, but also add a regularization term, which tries to make the topic assignments of corresponding documents close.

we use posterior regularization (Graca et al., 2008;Ganchev et al., 2009) to place linear constraints on the expectations of topic assignments to two corresponding documents.


But unlike in phrase-based decoding, we can not simply choose any arbitrary uncovered subtree for the next step, since rules already dictate which subtree to visit next.

what we need here is not really a tree coverage vector, but more of a derivation history.


This is because each prediction step now has O(n) choices, since they need to expand nodes like VP[1, 6] as: where the midpoint i in general has O(n) choices (just like in CKY).


Multipass coarse-to-fine decoding is another popular idea (Venugopal et al., 2007;Zhang and Gildea, 2008;Dyer and Resnik, 2010).

dyer and Resnik (2010) uses a two-pass approach, where their first-pass, −LM decoding is also incremental and polynomial-time (in the style of Earley (1970) algorithm), but their second-pass, +LM decoding is still bottom-up CKY with cube pruning.


We would like to thank David Chiang, Kevin Knight, and Jonanthan Graehl for discussions and the anonymous reviewers for comments.

we are indebted to the reviewer who pointed out a crucial mistake in Theorem 1 and its proof in the submission.


Here we use them for supervised text classification.

we use adaptor grammars (Johnson et al., 2006), a formalism for nonparametric Bayesian modeling that has recently proven useful in unsupervised modeling of phonemes (Johnson, 2008), grammar induction (Cohen et al., 2010), and named entity structure learning (Johnson, 2010), to make supervised naïve Bayes classification nonparametric in order to improve perspective modeling.


There are, however, some differences between the model that we have described above and the standard naïve Bayes model depicted in Figure 1(a).

there is no longer a single choice of class per document; each sentence is assigned a class.


Compare that with isra palestinian, which is cached the same number of times but appears only 18 times in the corpus.

the sequence people go is more easily explained by two sequential unigrams, not a bigram.


Our techniques could be used on a variety of other complex word forms.

many of our features extend naturally to identifying stemstem compounds (like panfry or healthcare).


Note that the polysemy is of the syntactic form as a unit, not of individual words.

the verb 'play' is used in the same sense in both cases.


In the condition where the ASF type is not known at test time, MFS gives each form in the test set the sense that was the overall most frequent in the training set.

in this case the baseline gives all test set CSFs the same sense.


When the ASF type is known at test time, MFS gives each test CSF the most frequent sense of that ASF type in the training set.

in this case all CSFs having the same ASF type get the same sense, and forms of different types are guaranteed to get different senses.


In the last case the impact of POS features is larger.

when using an unconstrained classifier, POS features give more than ASF type information to to the model.


We saw no impact on model performance when using the constrained model, and a 0.5% decrease when using the unconstrained classifier.

our model does not require these lexical features, which is somewhat counter-intuitive.


Among these are various classifiers, ensemble methods combining several supervised classifiers, bootstrapping and semi-supervised learning methods, using the Web as a corpus and knowledge-based methods relying mainly on machine readable dictionaries.

related to this paper are works that exploit syntax (Martinez et al., 2002;Tanaka et al., 2007) and ensemble methods (e.g.


Thus, here we address methods to make IE robust to such varied-quality inputs.

our overall goals are • to skip processing non-language material such as standard or database-specific mark-up, • to process all non-target-language text cautiously, catching interspersed target-language text as well as text which is compatible with the target language, e.g.


We systematically evaluate our stratified bootstrapping approach in the semantic relation classification subtask of the ACE RDC (Relation Detection and Classification) task.

we compare various clustering algorithms on the stratified bootstrapping performance.


The motivations behind our approach are that the unlabeled data can be partitioned into a number of strata using a clustering algorithm and that representative and diverse seeds can be derived from such strata in the framework of stratified sampling (Neyman, 1934) for an oracle to annotate.

we employ a diversity-motivated intra-stratum sampling scheme to pick a center and additional instances as seeds from each stratum.


To our knowledge, the USPT has achieved the best performance in relation extraction so far on the ACE RDC 2004 corpus.

we use the SVM light -TK 1 package as our classifier.


As a simple yet effective clustering method, the Kmeans algorithm assigns each instance to the cluster whose center (also called centroid) is nearest.

the center is the average of all the instances in the cluster, i.e., with its coordinates the arithmetic means for each dimension separately over all the instances in the cluster.


Besides, the final flat cluster structures given a special number of clusters are generated greedily from the cluster hierarchy by maximizing the equal distribution of instances among different clusters.

when the cluster number reaches a certain threshold, the dense area will get more seeds represented in the seed set.


Besides, the performance of clustering-based semisupervised relation classification is also measured over other typical cluster numbers (i.e., 1, 50, 60, 80, 100).

when the cluster number equals 1, it means that only diversity other than representativeness is considered in the seed sampling.


This further verifies the aforementioned analysis.

as a hierarchical clustering algorithm, HAC can sample seeds that better capture the distribution of the training data.


Semantic drift is reduced by forcing the categories to be mutually exclusive.

candidate terms can only be extracted by a single category and patterns can only extract terms for a single category.


(2007) and McIntosh (2010) manually crafted negative categories that were necessary to prevent semantic drift.

in McIntosh (2010), a biomedical expert spent considerable time (∼15 days) and effort identifying potential negative categories and subsequently optimising their associated seeds in trial and error bootstrapping runs.


The performance improvements so far using NEG-FINDER have been limited by the time at which new negative categories are discovered and incorporated into the bootstrapping process.

system improvements can only be gained from the negative categories after they are generated.


All categories, except TUMOUR, improve significantly with the inclusion of the discovered negative categories.

the CELL and SIGN categories, which are affected severely by semantic drift, increase by up to 33.3% and 45.2%, respectively.


For the multiaspect summarization task, y * consists of sequence labels (e.g., value or service) for the tokens of a document.

the document text s is composed of sentences s 1 , .


Multi-Aspect Review Summarization The goal of this task is to extract informative phrases that identify information relevant to several predefined aspects of interest.

we would like our system to both extract important phrases (e.g., cheap food) and label it with one of the given aspects (e.g., value).


Therefore, we need to leverage this useful information in a principled way to get the best out of our models.

we need to capture the conversation structure at the fragment (quotation) level and to incorporate this structure into our models.


To regularize LDA with FQG, we form the word network where a word is connected to the words in the same or adjacent fragments.

if word w i ∈ f rag x and word w j ∈ f rag y (w i = w j ), we create a link (w i , w j ) if x = y or (x, y) ∈ E, where E is the set of edges of the FQG.


However, whereas the difference of scores between the two methods is obvious for small amounts of training data, this difference progressively decreases as we increase the amount of training data.

with 100 training instances, the difference between baseline and proposed method is the largest: For RSTDT, the baseline has a macro-averaged F-score of 0.084, whereas the the proposed method has a macro-averaged Fscore of 0.189 (ca.


As in the RSTDT experiment, the benefit of us- ing unlabeled data is more obvious when the number of labeled training instances is small.

with 100 training instances, the maximum improvement in F-score is 459% (corresponds to 100000 unlabeled instances).


Although the macro-average F-scores of the classifiers described are too low to be used directly as discourse analyzers, the gain in F-score and accuracy for small labeled datasets are a promising perspective for improving classification accuracy for infrequent relation types.

the proposed method can be employed in existing discourse classifiers that work well on popular relations, and be expected to improve the overall accuracy.


To capture both effects, we model language use as a cooperative game between two players: a speaker, who generates an utterance, and a listener, who responds with an action.

we consider the task of generating spatial references to objects, wherein the listener must accurately identify an object described by the speaker.


This section focuses on an orthogonal direction: improving literal strategies with learning.

we construct learned strategies from log-linear models trained on human annotations.


• Projection functions measure the relative position of the bounding boxes with respect to one another.

let v be the vector from the center of w.o to the center of o.


We now extend these strategies to handle more complex utterances.

we consider utterances that conform to the following grammar: 4 [noun] This grammar captures two phenomena of language use, conjunction and relativization.


Compositional semantics allows us to define the interpretation of complex utterances in terms of simpler ones.

each node in the parse tree has a denotation, which is computed recursively in terms of the node's children via a set of simple rules.


Notice that the rational speaker as defined so far does not make full use of our grammar.

the rational speaker will never use the "wildcard" noun something nor the relativization rule in the grammar because an NP headed by the wildcard something can always be replaced by the object ID to obtain a higher utility.


It also can be seen that the improvement on 2.1 million sentence-pairs is less than that of the 20K and 200K data sets.

as the size of the training data increases, the problems of data sparseness decrease, so that the coverage of the test set by the parallel corpus will correspondingly increase.


We argue that the reason that the "Lattice" system selects P P 2 and T 2 rather than P P 1 and T 1 is because of the weight estimation in the lattice.

p p 2 is more prioritised, while p p 1 is more penalised based on equation 4.


Our key contribution is to replace the use of scarce resources of bilingual co-occurrences with the use of untapped and significantly larger resources of monolingual co-occurrences for translation.

we extract monolingual cooccurrences of entities from English and Chinese Web corpora, which are readily available from entity search engines such as PeopleEntityCube 1 , deployed by Microsoft Research Asia.


Though we illustrate with English-Chinese pairs in the paper, our method can be easily adapted to other language pairs.

we propose a novel approach of abstracting entity translation as a graph matching problem of two graphs G e and G c in Figures 1(a) and (b).


In particular, we propose a novel approach of abstracting entity translation as a graph matching problem of two graphs G e and G c in Figures 1(a) and (b).

the similarity between two nodes v e and v c in G e and G c is initialized as their transliteration similarity, which is iteratively refined based on relational similarity obtained from monolingual cooccurrences.


To highlight the advantage of our proposed approach, we compare our results with commercial machine translators (1) Engkoo 3 developed in Microsoft Research Asia and (2) Google Translator 4 .

figure 2 reports the precision for two groups-"heads" that belong to top-100 popular people (determined by the number of hits), among randomly sampled 304 people names 5 from six graph pairs of size 1,000 each, and the remaining "tails".


The complementary strength of the above two approaches naturally calls for a holistic approach, such as recent work combining transliterationand corpus-based similarity mining bilingual cooccurrences using general search engines.

(Al-Onaizan and Knight, 2002) uses transliteration to generate candidates and then web corpora to identify translations.


Such expansion enables to translate "long-tail" people entities with non-trivial Web occurrences in each monolingual corpus, but not much bilingual co-occurrences.

we initialize name pair similarity using transliteration-based approach, and iteratively reinforces base similarity using relational similarity.


Moreover, since words in N e are transliterated into characters in P Y c independently, it is more accurate to compute the ED between N e and P Y c , i.e., ED name (N e , P Y c ), as the sum of the EDs of all component transliteration pairs, i.e., every e i in N e and its corresponding transliteration (s i ) in P Y c .

we need to first align all s j 's in P Y c with corresponding e i in N e based on whether they are translations of each other.


From the initial similarity, we model our problem as an iterative approach that iteratively reinforces the similarity R ij of the nodes i and j from the matching similarities of their neighbor nodes u and v. The basic intuition is built on exploiting the similarity between monolingual co-occurrences of two different languages.

we assume two entities with strong relationship co-occur frequently in both corpora.


First, we crawled G e = (V e , E e ) and G c = (V c , E c ) from English and Chinese EntityCubes.

we built a graph pairs (G e , G c ) expanding from a "seed pair" of nodes s e ∈ V e and s c ∈ V c until the number of nodes for each graph becomes 1,000 6 .


By using this procedure, we built six graph pairs from six different seed pairs.

the six seed nodes are English names and its corresponding Chinese names representing a wide range of occupation domains (e.g., 'Barack Obama,' 'Bill Gates,' 'Britney Spears,' 'Bruno Senna,' 'Chris Paul,' and 'Eminem') as Table 1 depicts.


With the re-constructed rule set, during decoding, we generate two target translation hypothesis spaces (in the form of packed forests) synchronously by the tree-to-string rules and tree-to-tree sequence rules, and maintain the projection between them.

we generate hypothesis (searching) from the tree-to-string forest and calculate the probability (evaluating syntax goodness) for each hypothesis by the hyper-edges in the tree-to-tree sequence forest.


Crucially, to compute these features, it is sufficient to count repetitions of a term.

for many IR-like tasks, there is no need for an automatic speech recognition (ASR) system to label terms with phonemes and/or words.


More sophisticated edge weighting schemes would likely provide benefit.

we expect improved clustering by introducing weights that reflect acoustic similarity between match intervals, rather than relying solely upon the term discovery algorithm to make a hard decision.


The global metric is computed by taking the weighted average of the entropy of the members of each cluster.

entropy(C, L) is given by: where N i is the number of instances in cluster i, P (c i , l j ) is the probability of seeing label l j in cluster c i and the other variables are defined as above.


This baseline is based on a vanilla phone recognizer on top of the same MLP-based acoustic model (see Section 5 and the references therein for details) used to discover the pseudo-terms.

the phone posteriorgrams were transformed to frame-level monophone state likelihoods (through division by the framelevel priors).


Alternative ASR hypotheses have been used in many different ways in speech driven systems.

in (Mangu et al., 2000) multiple lattice alignment is used for construction of word confusion networks and in (Hakkani-Tür et al., 2006) word confusion networks are used for named entity detection.


In this section, we elaborate on the potential error sources.

we discuss two types of error: (1) a referring expression is incorrectly recognized or (2) a recognized referring expression is not resolved to a correct referent object set.


8 However, it should be noted that any automatic metric could be used instead of ROUGE.

we use ROUGE 1 (R-1), ROUGE 2 (R-2) and ROUGE SU4 (R-SU4).


For example, this ordering favors the correct candidate (pepsi) for the mention they: In a significant departure from previous work, each model in our framework gets (possibly incomplete) clustering information for each mention from the earlier coreference models in the multi-pass system.

each mention m i may already be assigned to a cluster C j containing a set of mentions: Unassigned mentions are unique members of their own cluster.


After T i has been chosen, each field in the template is replaced with a word given the corresponding field value in the world state.

a word is chosen from the parameters learned in the model of Liang et al.


Therefore, we also create a BASE template which only abstracts a subset of the fields.

we define a trigger pattern which specifies a simple condition under which a field should be abstracted.


Our approach therefore uses an ILP formulation which will provide a globally optimal solution, and which can be efficiently solved using standard optimization tools.

the model selects phrases and paraphrases from which to form the output sentence.


We address the modeling, parameter estimation and search challenges that arise from the introduction of reordering models that capture non-local reordering in alignment modeling.

we introduce several reordering models that utilize (pairs of) function words as contexts for alignment reordering.


To measure the function words' potential to predict non-monotone reorderings, we examined the border words where two adjacent blocks meet.

we are interested in how many adjacent blocks whose border words are function words.


Formally, this model takes the form of probability distribution , which conditions the reordering on the lexical identity of the function word alignment (but independent of the lexical identity of its neighboring phrases).

o maps the reordering into one of the following four orientation values (borrowed from Nagata et al.


We used GIZA++, the implementation of the defacto standard IBM alignment model, as our baseline alignment model.

we used GIZA++ to align the concatenation of the development set, the test set, and the unaligned corpora, with 5, 5, 3 and 3 iterations of model 1, HMM, model 3, and model 4 respectively.


For our translation model, we used the standard features based on the relative frequency counts, including a 5-gram language model feature trained on the English portion of the whole training data plus portions of the Gigaword v2 corpus.

we tuned the weights of these features via MERT on the NIST MT06 set and we report the result on the NIST MT02, MT03, MT04 and MT05 sets.


We use the notion of function words to infer such regularities, resulting in several reordering models that are employed as features in a discriminative alignment model.

our models predict the reordering of function words by looking at their dependencies with respect to their neighboring phrases, their neighboring function words, and the sentence boundaries.


In order to simplify hierarchical phrase-based grammars and make translation feasible with relatively large parallel corpora, some authors discuss the need for various filters during rule extraction (Chiang, 2007).

lopez (2008) enforces a minimum span of two words per nonterminal,  use a minimum count threshold for all rules, and Iglesias et al.


This more exhaustive rule extraction method permits a grammar simplification, as expressed by the phrase movement allowed by its rules.

a simple grammar with rules of only one nonterminal is shown to outperform a more complex grammar built on rules extracted from Viterbi alignments.


We propose a method to integrate the MaxEnt models into an HPB system.

: • For hierarchical rules, we classify the sourceside and the target-side into 7 and 17 reordering patterns, respectively.


First, a change in domain will be signaled by a change in the feature distributions.

new words, phrases, syntactic structures, etc.


The A-distance is distribution independent.

it makes no assumptions about the form of the underlying distribution nor about the form of the change that might occur, either algorithmically or in the underlying theory.


As a useful baseline for comparison, we can measure using supervised sources of information, where additional information is provided that is not normally available.

we investigate two types of supervised knowledge: the labels of examples in the stream and knowledge of the target domain.


We allow each source word to align with exactly one target word, but each target word may align with multiple source words.

the fertility φ i of a word e i at position i is defined as the number of aligned source words: where δ is the Kronecker delta function: the fertility of all empty words in the target sentence is 2I+1 i=I+1 φ i .


This situation does not change when one considers all the sentences in a development set instead of just one: while varying theλ vector, the BLEU score changes only when there is a change at the top of the ranking of the alternatives for at least one sentence in the set.


While this strategy was shown to be superior to random as well as n-gram based dissimilarity selection, its coarse granularity (reducing a candidate sentence to a lowdimensional feature vector for ranking) makes it unsuitable for many situations.

it is seen to have little or no benefit over random selection when there is no logical separation of the candidate pool into "in-domain" and "out-of-domain" subsets.


Given a set R of rules and an input sentence on the fast jet ski of mr smith the ◊ ◊ of ◊ 0 1 2 3 4 5 6 7 8 Figure 7: An application context for the pattern "the ♦ ♦ of ♦" and the sentence "on the fast jet ski of mr smith".

s, find the minimum cost of any R-derivation that derives s. compute: Chart parsing solves Minimum Derivation Cost via dynamic programming.


It records this cost in an associative map referred to as the chart.

assume that we have computed and stored the minimum cost of every derivation class σ , X , γ , where X is a postcondition, γ is a carry, and σ is a proper subspan of σ.


The approach we take in this article is motivated by the fact that natural language allows for multiple text views on a given content, and that if two phrases are good paraphrases in context, then considering appropriate training examples of one of the phrases could provide larger quantities of training data for translating the other.

we hypothesize that there may be more training data to learn a phrase's translations in a bilingual corpus than what SMT approaches typically use.


We are using the terms "top-down" and "leftto-right" following e.g., (Abney and Johnson, 1991;Roark, 2001).

in top-down strategies a node is enumerated before any of its descendents.


Since Nivre's parser is a dependency parser this exact case does not apply (as it does not use CFG rules), but similar situations arise.

whenever a word is dependent on a word that appears later in the string, it remains unconnected on the stack until the second word appears.


That the tree is binary forces the decision tree to use information about words and nonterminals one bit at a time.

we can now ask for a little information about many different previous words in the sentence.


Developing a quality assessment algorithm for dependency trees will allow us to apply confidencebased ZL to unsupervised dependency parsing.

it will enable us to explore the combination of the methods proposed in (Spitkovsky et al., 2010) with ZL for the DMV model and to integrate the PUPA score into their bootstrapping algorithm.


1 (Malouf, 2002) The useful training data from this process is the parses from those sentences for which: more than one parse was found; and at least one parse has been annotated as correct.

there needs to be both gold and non-gold trees for any sentence to be used in training the discriminative model.


We show that dependency parsers have more difficulty parsing questions than constituency parsers.

deterministic shift-reduce dependency parsers, which are of highest interest for practical applications because of their linear running time, drop to 60% labeled accuracy on a question test set.


Instead, this paper explores scope learning from parse tree perspective and formulates it as a simplified shallow semantic parsing problem, which has been extensively studied in the past few years (Carreras and Màrquez, 2005).

the cue is recast as the predicate and the scope is recast as the arguments of the cue.


As far as scope learning considered, the cue can be regarded as the predicate 4 , while its scope can be mapped into several constituents dominated by the cue and thus can be regarded as the arguments of the cue.

given a cue and its scope which covers word m , …, word n , we adopt the following two heuristic rules to map the scope of the cue into several constituents which can be deemed as its arguments in the given parse tree.


Table 2 shows the features in better capturing the details regarding the argument candidate and the cue.

we categorize the additional features into three groups according to their relationship with the argument candidate (AC, in short) and the given cue/predicate (CP, in short).


Without loss of generality, we adopt a machine learning approach and train a classifier with supervised learning.

we make an independent classification for each word with a BIO label to indicate whether it is the first word of a cue, inside a cue, or outside of it, respectively.


Table 3 shows the features employed for cue recognition.

we categorize these features into three groups: 1) features about the cue candidate itself (CC in short); 2) features about surrounding words (SW in short); and 3) structural features derived from the syntactic parse tree (SF in short).


In addition, we also report an oracle performance to explore the best possible performance of our system by assuming that our scope finder can always correctly determine whether a candidate is an argument or not.

if an argument candidate falls within the golden scope, then it is a argument.


Table 10: Performance of both negation and speculation scope identification with automatic cues and automatic parse trees In this paper we have presented a new approach to scope learning by formulating it as a simplified shallow semantic parsing problem, which has been extensively studied in the past few years.

we regard the cue as the predicate and map its scope into several constituents which are deemed as arguments of the cue.